Integration of Artificial Intelligence by Tertiary Education Students in Zimbabwe: A Case of Responsibility and Accountability in Academic Writing
Bibliographic record
Abstract
This study examined how students utilise various AI tools responsibly and accountably in their academic writing. The study adopted a qualitative approach with data collected using an electronic questionnaire from self-selected participants. The questionnaire was uploaded to Google Forms, and a total of twenty-five participants took part in the study until data saturation was reached. Atlas. ti was utilised for data analysis due to its robust analytical capabilities. The findings highlighted the importance of responsible and accountable use of AI tools, as these practices enhance students' intellectual capabilities and foster innovation. The findings also indicate that use of AI tools play a crucial role in the lives of tertiary education students by enhancing the quality of their academic writing. It improves grammar, structure, and coherence, making the content more readable. Additionally, the study uncovered several challenges that students face regarding the use of AI in education. The study provided recommendations on best practices for employing AI in academic writing for tertiary-level students in Zimbabwe. Additionally, it advocated for all tertiary institutions to invest in plagiarism detection software(s) to ensure that the use of AI tools is accompanied by accountability in all written coursework, thereby contributing to students' final degree classifications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".